TD eyes $150M in cost reductions – with help from AI

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Dive Brief:

-- TD expects to cut insurance claims costs by $150 million "in the medium term" through AI deployment, vendor optimization, fraud detection and process reengineering, CFO Kelvin Tran said Thursday during the bank’s quarterly earnings call.
-- The firm’s investments in process improvement, digital and artificial intelligence capabilities will reduce timelines for fraud detection and increase speed and accuracy in resolving claims, Tran said. The investments are also expected to reduce, by half, the time it takes to create financial plans.
-- The bank is targeting $1 billion in annual value from AI, CEO Raymond Chun said during the call. “A core tenet of our AI strategy is to build once and use many times, scaling AI through repeatable patterns that lead to faster AI deployments and reduced cost of delivery,” he said.
Dive Insight:

TD is continuing its AI investments across multiple use cases after the technology yielded $170 million in value for the bank in 2025.

TD added a generative AI virtual assistant in its contact centers last year. The Knowledge Management System navigates agent questions and provides responses while reducing client hold times and resolving issues, the bank said.

As the bank drives toward its target of $1 billion in annual value — a figure shared initially during TD’s investor day in September — Chun pointed to the bank’s strategy of scaling AI through repetition.

“We saw the benefits of this approach with our GenAI knowledge management solution, which we first introduced in our contact centers last year and have now deployed across our over 1,000 branches in Canada,” he said Thursday. Questions that previously had employees “jumping through screens are now answered in seconds,” he added.

TD is taking a similar approach with agentic AI and is scaling a project aimed at simplifying its real estate secured lending pre-adjudication process, Chun said.

Banks are investing heavily in AI as the technology is anticipated to trim industry costs by up to 20%, with agentic AI poised to have the most significant impact on banks’ operations, according to estimates from McKinsey & Co.

Traditional AI and machine learning capabilities are also top of mind for TD, said the bank’s U.S. CEO, Leo Salom.

“We implemented machine learning models in our transaction monitoring system last year and additional models will be deployed in our program over the coming quarters,” Salom said Thursday.

He said the bank also rolled out a data-driven financial crime risk evaluation methodology resulting in a more “sophisticated assessment of the bank’s financial crimes risk.”

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